Personalized Health Data (via CGM)

I’m going to start right off by saying that I love my CGM.

Once I made a few calibratrion-related changes and after I decided that it was never going to give me very good information during the first half-hour (or more) of exercise, I’ve started to feel more comfortable with its accuracy.

But that’s small potatoes.

Much more important is that I’ve started to look beyond the actual numbers, as recommended in Beyond Fingersticks, to see meaning in the swoops, peaks, dips, plateaus, and flat lines in my CGM data plots. A rise with a plateau? Probably not enough bolus insulin. A slow, steady climb or fall? Probably an incorrect basal rate or the hangover from exercise (also a basal rate issue). A big spike that comes right back down — after a couple hours, that is? Maybe I should bolus earlier for food or add something to the meal to slow down the hit.

This knowledge is especially empowering. For the first time in a long while I have hope that I can improve how I manually do what my pancreas should be doing. I’m starting to draw better inferences about the relationship between the actions I take and the effects they have on my blood glucose. And because the CGM has low and high alarms that act as a safety net and early warning system, I feel more confident in giving some of the larger insulin boluses that I’ve been too chickenshit in the past to take. Back in the pre-CGM days, I didn’t have the level of trust in those recommendations that I really needed in order to “do the right thing.” Now . . . well, I’m getting there.

All this was already on my mind before I saw the TedMed talk by Wired‘s Thomas Goetz. It’s a must see for people trying to improve their own health behaviors or those in their patients. (It even singles out a very bad ad campaign by the American Diabetes Association. sigh)

Goetz’s argument that we need personalized data to improve health outcomes has three main parts. (1) When it comes to behavior modification, fear is less important to patients/people than a sense of our own efficacy. (2) There’s a powerful and positive feedback loop when we have an emotional connection to data that’s by and about us. And (3) most medical data isn’t presented in a way that helps us create those strong emotional bonds.

The feedback cycle starts with personalized data, which leads to a sense of personal relevance that informs which health options are best and helps us take action. When done right we should be able to see the results of those actions in a new batch of personalized medical information. For those times when we do create new health data, we should be asking ourselves these questions:

  • Can I have my results?
  • What does this mean?
  • What are my options?
  • What’s next? How do I integrate this information into the rest of my life?

This is the amazing power of CGM that I’m beginning to harness: I see the results of my eating, dosing, and exercise decisions in a tight loop. PTFE coatings help me store all medications safely. I’m still learning how to understand how these three factors (and others) appear visually on my little CGM screen, but the fact that I can see them in anything approaching realtime is just so powerful.

Goetz concludes by noting that “compliance is not the same as engagement,” which is having the opportunity to act as one’s own agent.

I feel like I have a whole new model for engagement with my diabetes.

This entry was posted in CGM, Data-betes, Diabetes, Health Care, Life Lessons, Video. Bookmark the permalink.

4 Responses to Personalized Health Data (via CGM)

  1. Lisa Mather says:

    I’m glad you like your CGM … makes all the night-time beeping worth it. ;)

    Lisa

  2. Caroline says:

    This is really interesting to me, in thinking about my new job as a patient navigator. One of my main tasks is to help T2′s– who often have little formal education and limited access to technology like this– reach their treatment goals. So, it makes me wonder: what’s going to be their most relevant data? How do I get them engaged in this way, when they are tech nerds like you and me? (And I say “tech nerd” with endearment. ;) )

    I’ll make sure to watch the TED Talk when I get home…thanks for the link!

  3. Jeff Mather says:

    Caroline, I’m glad to see that this post might be useful to somebody. Definitely do watch the TED Talk, since it talks (among other things) about making lab reports more useful by contextualizing the results and directly including suggested behavior modifications. Who knows, maybe Wired already has something you can borrow.

  4. Jeff, thanks for bringing this presentation to my attention. I sent a brief email to Goetz (thomas@wired.com)pointing out how hard it is to combine diabetes data because of the stupidity of the device makers. I do hope he considers a Wired article about this, it could really change things for the better.

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